Abstract
This paper presents a framework to process and analyze data from a pulse oximeter which remotely measures pulse rate and blood oxygen saturation from a set of individuals. Using case-based reasoning (CBR) as the backbone to the framework, records are analyzed and categorized according to their similarity. Record collection has been performed using a personalized health profiling approach in which participants wore a pulse oximeter sensor for a fixed period of time and performed specific activities for pre-determined intervals. Using a variety of feature extraction methods in time, frequency, and time-frequency domains, as well as data processing techniques, the data is fed into a CBR system which retrieves most similar cases and generates an alarm according to the case outcomes. The system has been compared with an expert's classification, and a 90% match is achieved between the expert's and CBR classification. Again, considering the clustered measurements, the CBR approach classifies 93% correctly both for the pulse rate and oxygen saturation. Along with the proposed methodology, this paper provides a basis for which the system can be used in the analysis of continuous health monitoring and can be used as a suitable method in home/remote monitoring systems.
Highlights
Today, the possibility to remotely monitor physiological health parameters provides a new approach for disease prevention and early detection [1, 2]
In developing health monitoring systems, several intelligent data processing methods have been proposed in the literature, for instance, neural network (NN) [4] and support vector machine (SVM) [5]
A clinical decision support system (CDSS) has been proposed where case-based reasoning (CBR) approach [6] is applied to analyze and process the data coming from a pulse oximeter that contain measurements of both pulse rate and blood oxygen saturation
Summary
The possibility to remotely monitor physiological health parameters provides a new approach for disease prevention and early detection [1, 2]. A clinical decision support system (CDSS) has been proposed where case-based reasoning (CBR) approach [6] is applied to analyze and process the data coming from a pulse oximeter that contain measurements of both pulse rate and blood oxygen saturation. Studies that consider only continuous biomedical data (i.e. pulse rate) and respective signal processing methods can be found in [22, 23] All these works have demonstrated that the recent advancement in sensor technology could provide continuous detection of, for example, pulse, blood oximetry, and level of physical activity. Be used while there is no expert classification available, that is, if the CBR system failed to retrieve any similar cases with a higher similarity value as a threshold
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